# Simulator Calibration under Covariate Shift with Kernels

arXiv: https://arxiv.org/abs/1809.08159 · HTML: https://mlsanity.com/paper/1809.08159/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [T-Cal: An optimal test for the calibration of predictive models](https://mlsanity.com/paper/2203.01850/) (2022)
- [Scalable Causal Domain Adaptation](https://mlsanity.com/paper/2103.00139/) (2021)
- [Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set](https://mlsanity.com/paper/2105.12893/) (2021)

## Abstract (arXiv)

We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift. Covariate shift is the situation where input distributions for training and test are different, and ubiquitous in applications of simulations. Our approach is based on Bayesian inference with kernel mean embedding of distributions, and on the use of an importance-weighted reproducing kernel for covariate shift adaptation. We provide a theoretical analysis for the proposed method, including a novel theoretical result for conditional mean embedding, as well as empirical investigations suggesting its effectiveness in practice. The experiments include calibration of a widely used simulator for industrial manufacturing processes, where we also demonstrate how the proposed method may be useful for sensitivity analysis of model parameters.